Vehicle Path Recognition Approach Based on Incomplete Automatic Vehicle Identification

Vehicle path recognition is one of the key methods used in urban traffic research, such as traffic flow characteristics analysis. Automatic vehicle identification (AVI) is often used for vehicle path recognition and is suitable for mixed traffic flow with connected automated vehicles (CAVs). However, there still remain issues in overcoming the difficulty of vehicle path identification caused by the discontinuity of AVI data and solving the problem of low precision of AVI application. To model the vehicle path, this paper selects the AVI system of Yicheng Town, Linfen City, Shanxi Province, as a test bed. The travel modes of private cars and taxis are discussed, and the quantified indicators of the model are determined. By combining the analytic hierarchy process (AHP) with the entropy weight method (EWM) to get the weights of the indicators, the path recognition model under incomplete AVI data is proposed. Finally, based on the path recognition model proposed in this paper, case studies are carried out for the private car and taxi path recognition, respectively. The validity of the path identification through practical studies and the effect of the number of missing nodes of AVI equipment on the accuracy of the model are discussed. The results show that the recognition of the travel path using the proposed model is consistent with the actual travel path. The accuracy of the proposed model is more than 60% when the number of missing nodes is less than 7 in total 31 nodes. Considering the decision models for private cars and taxis, respectively, the proposed model provides a method for vehicle path recognition based on incomplete AVI data.